Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex-valued processing Then, the challenges of multichannel processing of complex-valued signals are explored This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension Key Features: Book emphasizes important applications and theoretical advances, eg, complex-valued signal processing Book examines the seven most important topics in adaptive filtering that will define the next generation adaptive filtering solutions All contributors are acknowledged leaders in the subjects of their contributions Book have end of chapter problems About the Author Tulay Adali PhD, is Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. Professor Adali, earned his PhD in Electrical and Computer Engineering at North Carolina State University in 1992. Dr. Adali's research interests are in statistical signal processing, neural computation, adaptive signal processing, biomedical data analysis, ormatics and communications. Simon Haykin, PhD, is Professor and Director of Neurocomputation for Signal Processing at McMaster University. Simon Haykin is a noted authority on adaptive and learning systems. He has pioneered signal-processing techniques and systems for radar and communication applications, and authored several fundamental textbooks in those fields. From 1972 to 1993, he served as founding director of McMaster's Communications Research Laboratory. Continually developing new curricula, Dr. Haykin has created innovative courses in emerging fields: neural networks, Bayesian sequential state estimation and space-time communication theory. An IEEE Life Fellow and a Fellow of The Royal Society of Canada, Dr. Haykin has received the IEEE Signal Processing Society Education Award, the IEEE Education Society McGraw-Hill/Jacob Millman Award, the IEEE Region 7 McNaughton Gold Medal and the International Union of Radio Science's Booker Gold Medal. Table of Contents Complex-Valued Adaptive Signal Processing Introduction Preliminaries Optimization in the Complex Domain Widely Linear Adaptive Filtering Nonlinear Adaptive Filtering with Multilayer Perceptrons Complex Independent Component Analysis Summary Acknowledgment Problems References Robust Estimation Techniques for Complex-Valued Random Vectors Introduction Statistical Characterization of Complex Random Vectors Complex Elliptically Symmetric (CES) Distributions Tools to Compare Estimators Scatter and Pseudo-Scatter Matrices Array Processing Examples MVDR Beamformers Based on M -Estimators Robust ICA Conclusion Problems References Turbo Equalization Introduction Context Communication Chain Turbo Decoder: Overview Forward-Backward Algorithm Simplified Algorithm: Interference Canceler Capacity Analysis Blind Turbo Equalization Convergence Multichannel and Multiuser Settings Concluding Remarks Problems References Subspace Tracking for Signal Processing Introduction Linear Algebra Review Observation Model and Problem Statement Preliminary Example: Oja's Neuron Subspace Tracking Eigenvectors Tracking Convergence and Performance Analysis Issues Illustrative Examples Concluding Remarks Problems References Particle Filtering Introduction Motivation for Use of Particle Filtering The Basic Idea The Choice of Proposal Distribution and Resampling Some Particle Filtering Methods Handling Constant Parameters Rao--Blackwellization Prediction Smoothing Convergence Issues Computational Issues and Hardware Implementation Acknowledgments Exercises References Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems Introduction Back-Propagation and Support Vector Machine-Learning Algorithms: Review Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation The Extended Kalman Filter Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms Concluding Remarks Problems References Bandwidth Extension of Telephony Speech Introduction Organization of the Chapter Nonmodel-Based Algorithms for Bandwidth Extension Basics Model-Based Algorithms for Bandwidth Extension Evaluation of Bandwidth Extension Algorithms Conclusion Problems References Index